Spring AI Showcase Demo Project

This project demonstrates the integration of AI capabilities within a Spring Boot application, utilizing the Spring AI framework.

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Table of Contents

• Articles
• Architecture
• Running the Application

Articles

1. Getting started with Spring AI Chat Model and easily switch between different AI providers including OpenAI, Mistral AI and Ollama. The example is available in the branch master. A detailed guide may be found in the following article: Getting Started with Spring AI and Chat Model
2. Getting started with Spring AI Function Calling for OpenAI chat models. The example is available in the branch master. A detailed guide may be found in the following article: Getting Started with Spring AI Function Calling
3. Using RAG (Retrieval Augmented Generation) and Vector Store with Spring AI. The example is available in the branch master. A detailed guide may be found in the following article: Using RAG and Vector Store with Spring AI
4. Using Multimodality feature and Image Model with Spring AI and OpenAI. The example is available in the branch master. A detailed guide may be found in the following article: Spring AI with Multimodality and Images
5. Running multiple models with Ollama and integration through Spring AI. The example is available in the branch master. A detailed guide may be found in the following article: Using Ollama with Spring AI
6. Getting started with Spring AI Tool Calling for OpenAI/MistralAI chat models. The example is available in the branch master. A detailed guide may be found in the following article: Tool Calling with Spring AI
7. Integrate Spring AI with Azure OpenAI for chat models, image generation, tool calling and RAG. The example is available in the branch master. A detailed guide may be found in the following article: Spring AI with Azure OpenAI

Architecture

Currently, there are four @RestControllers that show Spring AI features:

pl.piomin.services.controller.PersonController - prompt template, chat memory, and structured output based on a simple example that asks AI model to generate some persons

pl.piomin.services.controller.WalletController - function calling that calculates a value of our wallet stored in local database in conjunction with the latest stock prices

pl.piomin.services.controller.StockController - RAG with a Pinecone vector store and OpenAI based on stock prices API

pl.piomin.services.controller.ImageController - image model and multimodality

The architecture is designed to be modular and scalable, focusing on demonstrating how AI features can be incorporated into Spring-based applications.

Running the Application

Follow these steps to run the application locally.

git clone https://github.com/piomin/spring-ai-showcase.git
cd spring-ai-showcase

By default, this sample Spring AI app connects to OpenAI. So, before running the app you must set a token:

export OPEN_AI_TOKEN=<YOUR_API_TOKEN>
mvn spring-boot:run

To enable integration with Mistral, we should activate the mistral-ai profile:

export MISTRAL_AI_TOKEN=<YOUR_API_TOKEN>
mvn spring-boot:run -Pmistral-ai

To enable integration with Ollama, we should activate the ollama-ai profile:

mvn spring-boot:run -Pollama-ai

Before that, we must run the model on Ollama, e.g.:

ollama run llava

To enable integration with Azure OpenAI, we should activate the azure-ai profile and activate the Spring Boot azure-ai profile:

mvn spring-boot:run -Pazure-ai -Dspring-boot.run.profiles=azure-ai

To enable integration with Anthropic Claude, we should activate the anthropic profile and activate the Spring Boot anthropic profile as well:

export ANTHROPIC_AI_TOKEN=<YOUR_API_TOKEN>
mvn spring-boot:run -Panthropic -Dspring-boot.run.profiles=anthropic

You should also export the Azure OpenAI credentials:

export AZURE_OPENAI_API_KEY=<YOUR_AZURE_OPENAI_API_KEY>

For scenarios with a vector store (StockController, ImageController) you need to export the following ENV:

export PINECONE_TOKEN=<YOUR_PINECONE_TOKEN>

For scenarios with a stock API (StockController, WalletController) you need to export the following ENV:

export STOCK_API_KEY=<YOUR_STOCK_API_KEY>

More details in the articles.

REST API Endpoints

The application exposes several REST API endpoints organized by functionality. Below is a comprehensive list of all available endpoints:

Person Management (/persons)

Demonstrates prompt templates, chat memory, and structured output generation.

Wallet Management (/wallet)

Demonstrates function calling with stock price calculations and wallet value analysis.

Stock Analysis (/stocks)

Demonstrates RAG (Retrieval Augmented Generation) with Pinecone vector store for stock market analysis.

Image Processing (/images)

Demonstrates image model capabilities, multimodality, and image generation/analysis.

Dynamic API (/api)

Demonstrates dynamic API generation with chat memory for any entity type.

Example Usage

# Generate a list of persons
curl http://localhost:8080/persons

# Calculate wallet value with AI tools
curl http://localhost:8080/wallet/with-tools

# Load stock data into vector store
curl http://localhost:8080/stocks/load-data

# Find stock with most growth
curl http://localhost:8080/stocks/v2/most-growth-trend

# Generate an image with cats
curl http://localhost:8080/images/generate/cats --output cats.png

# Generate dynamic list of cars
curl http://localhost:8080/api/cars

Requirements

• OpenAI/Mistral/Ollama: Required for all chat-based endpoints
• Stock API Key: Required for /wallet/* and /stocks/* endpoints
• Pinecone Token: Required for vector store operations (/stocks/*, /images/load, /images/generate-and-match/*)
• Image Model: Required for /images/generate/* and /images/generate-and-match/* endpoints